Overview

Dataset statistics

Number of variables9
Number of observations366
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory28.6 KiB
Average record size in memory80.0 B

Variable types

Numeric9

Alerts

FREQUENCIA BOMBA 1 is highly overall correlated with FREQUENCIA BOMBA 2 and 7 other fieldsHigh correlation
NIVEL DO RESERVATÓRIO - LT01 is highly overall correlated with FREQUENCIA BOMBA 1 and 6 other fieldsHigh correlation
VAZÃO DE ENTRADA- FT01 is highly overall correlated with FREQUENCIA BOMBA 1 and 6 other fieldsHigh correlation
VAZÃO DE GRAVIDADE - FT02 is highly overall correlated with FREQUENCIA BOMBA 1 and 6 other fieldsHigh correlation
VAZÃO DE RECALQUE - FT03 is highly overall correlated with FREQUENCIA BOMBA 1 and 7 other fieldsHigh correlation
PRESSÃO DE SUCÇÃO - PT01 is highly overall correlated with FREQUENCIA BOMBA 1 and 6 other fieldsHigh correlation
PRESSÃO DE RECALQUE - PT02 is highly overall correlated with FREQUENCIA BOMBA 1 and 6 other fieldsHigh correlation
FREQUENCIA BOMBA 2 is highly overall correlated with FREQUENCIA BOMBA 1 and 7 other fieldsHigh correlation
FREQUENCIA BOMBA 3 is highly overall correlated with FREQUENCIA BOMBA 1 and 2 other fieldsHigh correlation
NIVEL DO RESERVATÓRIO - LT01 has unique valuesUnique
VAZÃO DE ENTRADA- FT01 has unique valuesUnique
VAZÃO DE GRAVIDADE - FT02 has unique valuesUnique
VAZÃO DE RECALQUE - FT03 has unique valuesUnique
PRESSÃO DE SUCÇÃO - PT01 has unique valuesUnique
PRESSÃO DE RECALQUE - PT02 has unique valuesUnique
FREQUENCIA BOMBA 1 has 8 (2.2%) zerosZeros
FREQUENCIA BOMBA 2 has 70 (19.1%) zerosZeros
FREQUENCIA BOMBA 3 has 227 (62.0%) zerosZeros

Reproduction

Analysis started2022-12-14 14:43:14.575534
Analysis finished2022-12-14 14:43:38.981624
Duration24.41 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

FREQUENCIA BOMBA 1
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct356
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.932082
Minimum0
Maximum57.884742
Zeros8
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2022-12-14T11:43:39.191070image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.3437257
Q133.595225
median52.689572
Q354.726129
95-th percentile56.667869
Maximum57.884742
Range57.884742
Interquartile range (IQR)21.130904

Descriptive statistics

Standard deviation17.624569
Coefficient of variation (CV)0.42031228
Kurtosis-0.25952149
Mean41.932082
Median Absolute Deviation (MAD)3.6242802
Skewness-1.0810598
Sum15347.142
Variance310.62544
MonotonicityNot monotonic
2022-12-14T11:43:39.465989image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8
 
2.2%
10.45626259 2
 
0.5%
6.707071304 2
 
0.5%
16.33021418 2
 
0.5%
53.90830898 1
 
0.3%
0.07812268396 1
 
0.3%
34.78656371 1
 
0.3%
7.864561024 1
 
0.3%
37.20146571 1
 
0.3%
57.67244562 1
 
0.3%
Other values (346) 346
94.5%
ValueCountFrequency (%)
0 8
2.2%
0.07812268396 1
 
0.3%
0.1135240048 1
 
0.3%
1.457959493 1
 
0.3%
1.458722432 1
 
0.3%
2.138453166 1
 
0.3%
2.416199684 1
 
0.3%
5.207150777 1
 
0.3%
5.62386322 1
 
0.3%
5.790993781 1
 
0.3%
ValueCountFrequency (%)
57.88474162 1
0.3%
57.80188862 1
0.3%
57.78695647 1
0.3%
57.72492854 1
0.3%
57.67244562 1
0.3%
57.64668687 1
0.3%
57.41708151 1
0.3%
57.24754477 1
0.3%
57.23739465 1
0.3%
57.12615188 1
0.3%

FREQUENCIA BOMBA 2
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct297
Distinct (%)81.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.78064
Minimum0
Maximum53.488396
Zeros70
Zeros (%)19.1%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2022-12-14T11:43:39.767413image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16.3856964
median16.350328
Q323.106649
95-th percentile32.569798
Maximum53.488396
Range53.488396
Interquartile range (IQR)16.720952

Descriptive statistics

Standard deviation11.487194
Coefficient of variation (CV)0.7279295
Kurtosis0.46790366
Mean15.78064
Median Absolute Deviation (MAD)7.6099457
Skewness0.46529606
Sum5775.7143
Variance131.95562
MonotonicityNot monotonic
2022-12-14T11:43:40.090607image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 70
 
19.1%
16.29283444 1
 
0.3%
12.2011315 1
 
0.3%
13.17303356 1
 
0.3%
11.47718573 1
 
0.3%
12.5148557 1
 
0.3%
21.22075748 1
 
0.3%
11.57950465 1
 
0.3%
7.84309721 1
 
0.3%
26.73429219 1
 
0.3%
Other values (287) 287
78.4%
ValueCountFrequency (%)
0 70
19.1%
1.041552226 1
 
0.3%
1.249832153 1
 
0.3%
1.379900932 1
 
0.3%
1.458112081 1
 
0.3%
2.101109505 1
 
0.3%
2.189982891 1
 
0.3%
2.697555224 1
 
0.3%
2.707944234 1
 
0.3%
2.811360359 1
 
0.3%
ValueCountFrequency (%)
53.48839553 1
0.3%
52.76372433 1
0.3%
52.23364218 1
0.3%
50.31093295 1
0.3%
50.30937306 1
0.3%
50.05674442 1
0.3%
47.95462648 1
0.3%
47.28153245 1
0.3%
46.25633566 1
0.3%
46.10485204 1
0.3%

FREQUENCIA BOMBA 3
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct133
Distinct (%)36.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2133666
Minimum0
Maximum46.841334
Zeros227
Zeros (%)62.0%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2022-12-14T11:43:40.559461image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q38.6363372
95-th percentile30.022477
Maximum46.841334
Range46.841334
Interquartile range (IQR)8.6363372

Descriptive statistics

Standard deviation10.851065
Coefficient of variation (CV)1.7464067
Kurtosis1.5582587
Mean6.2133666
Median Absolute Deviation (MAD)0
Skewness1.6685356
Sum2274.0922
Variance117.74562
MonotonicityNot monotonic
2022-12-14T11:43:40.848373image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 227
62.0%
9.664798737 5
 
1.4%
7.248599052 3
 
0.8%
4.832399368 2
 
0.5%
5.084322276 1
 
0.3%
0.04579362473 1
 
0.3%
0.1263656343 1
 
0.3%
0.2069376465 1
 
0.3%
0.2875096562 1
 
0.3%
0.368081669 1
 
0.3%
Other values (123) 123
33.6%
ValueCountFrequency (%)
0 227
62.0%
0.02251497004 1
 
0.3%
0.02712027091 1
 
0.3%
0.04579362473 1
 
0.3%
0.1263656343 1
 
0.3%
0.1318343282 1
 
0.3%
0.1621923558 1
 
0.3%
0.2069376465 1
 
0.3%
0.2614307205 1
 
0.3%
0.2875096562 1
 
0.3%
ValueCountFrequency (%)
46.84133418 1
0.3%
44.46157153 1
0.3%
41.99538366 1
0.3%
40.82297285 1
0.3%
38.32062356 1
0.3%
36.97235439 1
0.3%
35.44564088 1
0.3%
34.455494 1
0.3%
33.53810469 1
0.3%
32.51348416 1
0.3%

NIVEL DO RESERVATÓRIO - LT01
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct366
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6079105
Minimum1.4588396
Maximum4.255611
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2022-12-14T11:43:41.186269image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1.4588396
5-th percentile2.6591137
Q13.3485832
median3.7397424
Q33.9915299
95-th percentile4.1673674
Maximum4.255611
Range2.7967713
Interquartile range (IQR)0.64294674

Descriptive statistics

Standard deviation0.4843132
Coefficient of variation (CV)0.13423648
Kurtosis1.4114045
Mean3.6079105
Median Absolute Deviation (MAD)0.29286115
Skewness-1.1700151
Sum1320.4952
Variance0.23455927
MonotonicityNot monotonic
2022-12-14T11:43:41.454186image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.875286599 1
 
0.3%
4.194900165 1
 
0.3%
3.598548005 1
 
0.3%
4.09392638 1
 
0.3%
3.792635779 1
 
0.3%
3.572079519 1
 
0.3%
4.167379707 1
 
0.3%
4.167330503 1
 
0.3%
3.739502043 1
 
0.3%
4.187361648 1
 
0.3%
Other values (356) 356
97.3%
ValueCountFrequency (%)
1.458839649 1
0.3%
1.963316371 1
0.3%
1.975009809 1
0.3%
2.107955938 1
0.3%
2.146511436 1
0.3%
2.21533224 1
0.3%
2.243839602 1
0.3%
2.282457704 1
0.3%
2.353388901 1
0.3%
2.360445827 1
0.3%
ValueCountFrequency (%)
4.255610963 1
0.3%
4.238957544 1
0.3%
4.234671116 1
0.3%
4.22364532 1
0.3%
4.218583852 1
0.3%
4.203865339 1
0.3%
4.201150844 1
0.3%
4.19982486 1
0.3%
4.196092864 1
0.3%
4.194925229 1
0.3%

VAZÃO DE ENTRADA- FT01
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct366
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean203.9386
Minimum22.853874
Maximum301.86282
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2022-12-14T11:43:41.758974image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum22.853874
5-th percentile67.465472
Q1176.92321
median221.32677
Q3252.28832
95-th percentile279.87289
Maximum301.86282
Range279.00895
Interquartile range (IQR)75.365109

Descriptive statistics

Standard deviation65.822557
Coefficient of variation (CV)0.32275673
Kurtosis0.12273827
Mean203.9386
Median Absolute Deviation (MAD)37.080786
Skewness-1.0284619
Sum74641.528
Variance4332.609
MonotonicityNot monotonic
2022-12-14T11:43:42.025892image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
233.0458415 1
 
0.3%
70.89670902 1
 
0.3%
272.5505651 1
 
0.3%
68.22664969 1
 
0.3%
212.9264864 1
 
0.3%
271.7262105 1
 
0.3%
57.28904612 1
 
0.3%
176.8569556 1
 
0.3%
268.0390467 1
 
0.3%
69.14874734 1
 
0.3%
Other values (356) 356
97.3%
ValueCountFrequency (%)
22.85387429 1
0.3%
23.67098368 1
0.3%
45.13681835 1
0.3%
45.76276124 1
0.3%
45.82522564 1
0.3%
46.14894212 1
0.3%
46.38032131 1
0.3%
53.48451341 1
0.3%
53.76006223 1
0.3%
55.46804079 1
0.3%
ValueCountFrequency (%)
301.8628197 1
0.3%
292.774423 1
0.3%
291.8711243 1
0.3%
291.7827042 1
0.3%
290.2942263 1
0.3%
286.9505081 1
0.3%
286.6973852 1
0.3%
284.5989176 1
0.3%
284.3868116 1
0.3%
283.8818906 1
0.3%

VAZÃO DE GRAVIDADE - FT02
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct366
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean111.75689
Minimum3.5839761
Maximum181.56548
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2022-12-14T11:43:42.325876image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum3.5839761
5-th percentile51.945779
Q187.527397
median122.66958
Q3132.9382
95-th percentile154.80146
Maximum181.56548
Range177.9815
Interquartile range (IQR)45.410808

Descriptive statistics

Standard deviation32.625729
Coefficient of variation (CV)0.29193482
Kurtosis-0.13348699
Mean111.75689
Median Absolute Deviation (MAD)16.460591
Skewness-0.69494434
Sum40903.024
Variance1064.4382
MonotonicityNot monotonic
2022-12-14T11:43:42.625848image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101.8270885 1
 
0.3%
61.64856593 1
 
0.3%
153.072231 1
 
0.3%
55.18085019 1
 
0.3%
109.5066519 1
 
0.3%
154.2589423 1
 
0.3%
58.51367188 1
 
0.3%
79.11509991 1
 
0.3%
142.1610578 1
 
0.3%
61.69558716 1
 
0.3%
Other values (356) 356
97.3%
ValueCountFrequency (%)
3.58397611 1
0.3%
15.88854376 1
0.3%
33.55565643 1
0.3%
34.30063144 1
0.3%
34.70834827 1
0.3%
35.52933693 1
0.3%
37.73271974 1
0.3%
38.05954901 1
0.3%
38.89107927 1
0.3%
39.44496918 1
0.3%
ValueCountFrequency (%)
181.565478 1
0.3%
177.4906346 1
0.3%
173.0618277 1
0.3%
169.2136116 1
0.3%
166.4727999 1
0.3%
165.6624734 1
0.3%
165.2773012 1
0.3%
165.0567973 1
0.3%
163.15286 1
0.3%
162.393026 1
0.3%

VAZÃO DE RECALQUE - FT03
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct366
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92.048801
Minimum10.383355
Maximum143.98841
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2022-12-14T11:43:42.962744image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum10.383355
5-th percentile31.029249
Q173.518915
median105.68828
Q3112.03439
95-th percentile124.80256
Maximum143.98841
Range133.60506
Interquartile range (IQR)38.515479

Descriptive statistics

Standard deviation30.560818
Coefficient of variation (CV)0.33200669
Kurtosis-0.15897889
Mean92.048801
Median Absolute Deviation (MAD)10.116234
Skewness-0.9971956
Sum33689.861
Variance933.96358
MonotonicityNot monotonic
2022-12-14T11:43:43.259658image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
95.51771514 1
 
0.3%
32.02935272 1
 
0.3%
107.9638246 1
 
0.3%
34.73455082 1
 
0.3%
78.11610703 1
 
0.3%
127.0728067 1
 
0.3%
19.22242113 1
 
0.3%
67.13882851 1
 
0.3%
119.3609098 1
 
0.3%
26.08802277 1
 
0.3%
Other values (356) 356
97.3%
ValueCountFrequency (%)
10.38335467 1
0.3%
18.78395883 1
0.3%
19.01510628 1
0.3%
19.22242113 1
0.3%
20.09405685 1
0.3%
20.10896885 1
0.3%
20.59290862 1
0.3%
21.63480695 1
0.3%
22.6513927 1
0.3%
22.69457825 1
0.3%
ValueCountFrequency (%)
143.9884148 1
0.3%
141.122522 1
0.3%
140.1231352 1
0.3%
137.250213 1
0.3%
137.2110043 1
0.3%
136.7754704 1
0.3%
136.7724743 1
0.3%
133.4322859 1
0.3%
133.0077931 1
0.3%
131.2276457 1
0.3%

PRESSÃO DE SUCÇÃO - PT01
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct366
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5599323
Minimum2.5995952
Maximum5.4753866
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2022-12-14T11:43:43.546573image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2.5995952
5-th percentile3.5513354
Q14.2243679
median4.6631536
Q35.0149587
95-th percentile5.2617872
Maximum5.4753866
Range2.8757914
Interquartile range (IQR)0.79059089

Descriptive statistics

Standard deviation0.55684561
Coefficient of variation (CV)0.12211708
Kurtosis0.36096531
Mean4.5599323
Median Absolute Deviation (MAD)0.38219037
Skewness-0.81482312
Sum1668.9352
Variance0.31007704
MonotonicityNot monotonic
2022-12-14T11:43:43.809491image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.838139951 1
 
0.3%
5.32407101 1
 
0.3%
4.505640248 1
 
0.3%
5.210670809 1
 
0.3%
4.78086713 1
 
0.3%
4.371084839 1
 
0.3%
5.394903402 1
 
0.3%
5.213329951 1
 
0.3%
4.601421118 1
 
0.3%
5.376384755 1
 
0.3%
Other values (356) 356
97.3%
ValueCountFrequency (%)
2.599595224 1
0.3%
2.755907903 1
0.3%
2.801692863 1
0.3%
2.920023123 1
0.3%
2.973470807 1
0.3%
3.028406084 1
0.3%
3.110565007 1
0.3%
3.220167051 1
0.3%
3.223515431 1
0.3%
3.298963288 1
0.3%
ValueCountFrequency (%)
5.475386639 1
0.3%
5.469098727 1
0.3%
5.428008278 1
0.3%
5.420319339 1
0.3%
5.40986371 1
0.3%
5.409394821 1
0.3%
5.394903402 1
0.3%
5.376384755 1
0.3%
5.376057903 1
0.3%
5.329075277 1
0.3%

PRESSÃO DE RECALQUE - PT02
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct366
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.832988
Minimum0.83083199
Maximum22.693986
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2022-12-14T11:43:44.115270image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.83083199
5-th percentile3.402025
Q113.197534
median20.196981
Q321.119525
95-th percentile21.80671
Maximum22.693986
Range21.863154
Interquartile range (IQR)7.9219906

Descriptive statistics

Standard deviation6.3239312
Coefficient of variation (CV)0.37568678
Kurtosis0.048187475
Mean16.832988
Median Absolute Deviation (MAD)1.2285623
Skewness-1.2321814
Sum6160.8738
Variance39.992106
MonotonicityNot monotonic
2022-12-14T11:43:44.372715image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.27573272 1
 
0.3%
4.375565251 1
 
0.3%
19.17370315 1
 
0.3%
4.513263542 1
 
0.3%
14.23600788 1
 
0.3%
22.10226162 1
 
0.3%
2.476588329 1
 
0.3%
13.96529599 1
 
0.3%
21.46042641 1
 
0.3%
3.198171616 1
 
0.3%
Other values (356) 356
97.3%
ValueCountFrequency (%)
0.8308319853 1
0.3%
1.177278982 1
0.3%
1.503833353 1
0.3%
1.619448689 1
0.3%
1.921274748 1
0.3%
2.014124649 1
0.3%
2.069669306 1
0.3%
2.153511247 1
0.3%
2.236963258 1
0.3%
2.331033865 1
0.3%
ValueCountFrequency (%)
22.69398618 1
0.3%
22.42783284 1
0.3%
22.10226162 1
0.3%
22.06411393 1
0.3%
21.9943076 1
0.3%
21.98121619 1
0.3%
21.97036803 1
0.3%
21.9528203 1
0.3%
21.9524219 1
0.3%
21.94964409 1
0.3%

Interactions

2022-12-14T11:43:36.352609image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:19.314704image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:21.425424image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:23.804513image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:25.695434image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:27.695299image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:30.091563image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:32.252848image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:34.240260image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:36.563543image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:19.606615image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:21.640357image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:24.036441image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:25.914367image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:27.922230image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:30.391325image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:32.468782image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:34.487182image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:36.752487image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:19.839543image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:21.848292image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:24.237887image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:26.154350image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:28.155159image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:30.605261image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:32.676718image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:34.744103image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:36.939431image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:20.032483image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:22.055229image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:24.429830image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:26.376281image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:28.381089image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:30.831192image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:32.885101image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:34.947042image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:37.140810image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:20.247242image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:22.301174image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:24.630987image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:26.589334image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:28.633011image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:31.091111image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:33.105032image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:35.158977image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:37.526692image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:20.462510image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:22.561093image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:24.837723image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:26.800575image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:28.879935image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:31.328038image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:33.323068image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:35.433891image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:37.745244image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:20.708428image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:23.076935image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:25.067049image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:27.026505image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:29.142854image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:31.559967image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:33.551000image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:35.668819image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:37.948181image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:20.957358image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:23.307811image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:25.279984image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:27.248437image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:29.561726image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:31.797893image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:33.784926image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:35.899748image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:38.156117image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:21.220486image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:23.566586image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:25.502493image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:27.490362image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:29.835641image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:32.048816image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:34.045846image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:43:36.138675image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2022-12-14T11:43:44.601657image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2022-12-14T11:43:44.953481image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-12-14T11:43:45.540208image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-14T11:43:45.888323image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-14T11:43:46.288266image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-12-14T11:43:38.432033image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-14T11:43:38.808918image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

FREQUENCIA BOMBA 1FREQUENCIA BOMBA 2FREQUENCIA BOMBA 3NIVEL DO RESERVATÓRIO - LT01VAZÃO DE ENTRADA- FT01VAZÃO DE GRAVIDADE - FT02VAZÃO DE RECALQUE - FT03PRESSÃO DE SUCÇÃO - PT01PRESSÃO DE RECALQUE - PT02
Timestamp
2020-01-0153.90830916.2928340.0000003.875287233.045841101.82708995.5177154.83814021.275733
2020-01-0253.90821218.7732280.0000003.984869187.289982107.52598097.2321114.93443020.973658
2020-01-0353.59153219.3072130.0000003.875669204.170449108.89228098.0045434.82483820.976069
2020-01-0453.96236819.2729590.0000003.926454239.730663107.73989697.8422644.87251821.733308
2020-01-0552.87654215.2899890.0000004.044858217.360193118.92765999.7370574.98084419.667277
2020-01-0649.47862424.0876364.8323993.739983174.747738125.225558107.3348474.62708221.085845
2020-01-0750.51954021.2650754.3270693.368573224.250331115.595289103.7630604.27091021.118652
2020-01-0854.62978421.1261170.0000003.389164209.202807117.373529101.6587104.30518821.069600
2020-01-0954.78819519.7898310.0000003.421481235.552948121.103031104.5980924.30326420.917260
2020-01-1054.65380123.5957550.0000003.405377225.445354120.157491107.2631624.29270721.250233
FREQUENCIA BOMBA 1FREQUENCIA BOMBA 2FREQUENCIA BOMBA 3NIVEL DO RESERVATÓRIO - LT01VAZÃO DE ENTRADA- FT01VAZÃO DE GRAVIDADE - FT02VAZÃO DE RECALQUE - FT03PRESSÃO DE SUCÇÃO - PT01PRESSÃO DE RECALQUE - PT02
Timestamp
2020-12-2214.0389827.9044900.0000003.70034653.48451363.27256237.8284094.7944994.462368
2020-12-2357.08145135.9924060.0000002.545013260.922810152.082684116.4589363.37386121.444099
2020-12-2444.54935827.9934278.4239182.906787254.396596131.572730103.1638433.80320719.937727
2020-12-2552.12481419.1966900.0000003.958751244.321652113.99820492.0097394.93099919.936115
2020-12-2652.32372324.5718720.0000003.882665227.833759128.669075100.8638054.81296120.578570
2020-12-2750.53180913.2451380.0000004.139487221.111380125.41303190.9303705.12014418.466390
2020-12-2852.09169027.0434970.0000004.194925241.122370129.55073899.4529355.11915320.024225
2020-12-2952.19789627.2519140.0000004.013414209.975585125.79528399.2688924.94895020.154963
2020-12-3052.49404227.4913880.0000003.729430208.305455132.392801101.4722674.64374820.081885
2020-12-3145.61783227.0925837.0884653.775853226.161141124.05183197.1035594.71414920.237162